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20202026
most citedEvaluating Semantic Accuracy of Data-to-Text Generation with Natural Language Inference

10 citations · 17 across the 6 of their papers we have counts for

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cs.CL2026

AnimatedLLM: Explaining LLMs with Interactive Visualizations

Zdeněk Kasner, Ondřej Dušek

Large language models (LLMs) are becoming central to natural language processing education, yet materials showing their mechanics are sparse. We present AnimatedLLM, an interactive…

cs.CL2025

FreshTab: Sourcing Fresh Data for Table-to-Text Generation Evaluation

Kristýna Onderková, Ondřej Plátek, Zdeněk Kasner +1

Table-to-text generation (insight generation from tables) is a challenging task that requires precision in analyzing the data. In addition, the evaluation of existing benchmarks is…

cs.CL2025

LLMs as Span Annotators: A Comparative Study of LLMs and Humans

Zdeněk Kasner, Vilém Zouhar, Patrícia Schmidtová +7

Span annotation - annotating specific text features at the span level - can be used to evaluate texts where single-score metrics fail to provide actionable feedback. Until recently…

cs.CL2022

Neural Pipeline for Zero-Shot Data-to-Text Generation

Zdeněk Kasner, Ondřej Dušek

In data-to-text (D2T) generation, training on in-domain data leads to overfitting to the data representation and repeating training data noise. We examine how to avoid finetuning p…

cs.CL202010 cited

Evaluating Semantic Accuracy of Data-to-Text Generation with Natural Language Inference

Ondřej Dušek, Zdeněk Kasner

A major challenge in evaluating data-to-text (D2T) generation is measuring the semantic accuracy of the generated text, i.e. checking if the output text contains all and only facts…

cs.CL20201 cited

Data-to-Text Generation with Iterative Text Editing

Zdeněk Kasner, Ondřej Dušek

We present a novel approach to data-to-text generation based on iterative text editing. Our approach maximizes the completeness and semantic accuracy of the output text while lever…